Author
Dinabandhu Bhandari
Other affiliations: Indian Statistical Institute
Bio: Dinabandhu Bhandari is an academic researcher from Heritage Institute of Technology. The author has contributed to research in topics: Genetic algorithm & Entropy (information theory). The author has an hindex of 12, co-authored 20 publications receiving 907 citations. Previous affiliations of Dinabandhu Bhandari include Indian Statistical Institute.
Papers
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TL;DR: A new informative measure for discrimination between two fuzzy sets is introduced and it is shown that this discriminating measure reduces to the nonprobabilistic entropy of Deluca and Termini under a special condition.
323 citations
TL;DR: It has been shown that an EGA converges to the global optimal solution with any choice of initial population, and mutation operation has been found to be essential for convergence.
Abstract: In this article, the genetic algorithm with elitist model (EGA) is modeled as a finite state Markov chain. A state in the Markov chain denotes a population together with a potential string. Proof for the convergence of an EGA to the best chromosome (string), among all possible chromosomes, is provided here. Mutation operation has been found to be essential for convergence. It has been shown that an EGA converges to the global optimal solution with any choice of initial population.
199 citations
TL;DR: This work is an attempt to demonstrate genetic algorithms adaptivity and effectiveness for searching global optimal solutions in selecting an appropriate image enhancement operator automatically.
116 citations
TL;DR: A tailored version of the probability measure of a fuzzy event is used for image segmentation and its application to clustering problems is indicated and applied to an object extraction problem.
72 citations
TL;DR: Two algorithms based on a new conditional entropy measure of a partitioned image have been formulated using the Poisson distribution for the gray level instead of the commonly used normal distribution and are found to produce good results.
72 citations
Cited by
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TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.
3,527 citations
01 Sep 1999
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
Abstract: Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
2,877 citations
TL;DR: The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.
1,337 citations
TL;DR: This study is helpful for an appropriate use of existing evaluation methods and for improving their performance as well as for systematically designing new evalution methods.
1,117 citations
TL;DR: An extensive evaluation of the unsupervised objective evaluation methods that have been proposed in the literature are presented and the advantages and shortcomings of the underlying design mechanisms in these methods are discussed and analyzed.
996 citations